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Record W2034567831 · doi:10.5539/ass.v10n24p63

Managing Sustainable Development of Socio-Economic Systems

2014· article· en· W2034567831 on OpenAlexvenueno aff
Olga Viktorovna Lenkova, Veronika Vasilyevna Efremova, Sergej Vladimirovich Postarnak, Ruslan Olegovich Pogrebnoj, Sergej Igorevich Skipin, Ajdarbek Orazaevich Erezhepov

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Business Development Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsSustainable developmentBalance (ability)SustainabilityCompetition (biology)BusinessEconomic systemIdentification (biology)Environmental economicsIndustrial organizationEconomicsPolitical science

Abstract

fetched live from OpenAlex

Nowadays, the concept of sustainable development permeates the entire economy, affecting the development of certain commercial structures, local areas and even countries. As mentioned above, the stability of the whole system is derived from the stability of its constituent elements. Although, in practice, a direct relationship between these phenomena do not exist. Identification of these mechanisms will form a model of sustainable development management of socio-economic system based on the balanced development of its individual elements. This project is aimed at solving the problem of finding a balance between the need to develop socio-economic systems in modern conditions and sustainability of their operation. Crises of recent years have actualized questions the sustainability of commercial organizations, as showed no willingness of individual companies to major changes in the external environment, as well as proven strategies unsustainability developed earlier in their development. Meanwhile, in terms of increased competition in some markets and in some regions, as well as in terms of accelerating changes in the environment of non-standard search (

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0060.005
Open science0.0010.008
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.220
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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